Regional and seasonal response of a West Nile virus vector to climate change.

Climate change will affect the abundance and seasonality of West Nile virus (WNV) vectors, altering the risk of virus transmission to humans. Using downscaled general circulation model output, we calculate a WNV vector's response to climate change across the southern United States using process-based modeling. In the eastern United States, Culex quinquefasciatus response to projected climate change displays a latitudinal and elevational gradient. Projected summer population depressions as a result of increased immature mortality and habitat drying are most severe in the south and almost absent further north; extended spring and fall survival is ubiquitous. Much of California also exhibits a bimodal pattern. Projected onset of mosquito season is delayed in the southwestern United States because of extremely dry and hot spring and summers; however, increased temperature and late summer and fall rains extend the mosquito season. These results are unique in being a broad-scale calculation of the projected impacts of climate change on a WNV vector. The results show that, despite projected widespread future warming, the future seasonal response of C. quinquefasciatus populations across the southern United States will not be homogeneous, and will depend on specific combinations of local and regional conditions.

Environmental predictors of seasonal influenza epidemics across temperate and tropical climates.

Human influenza infections exhibit a strong seasonal cycle in temperate regions. Recent laboratory and epidemiological evidence suggests that low specific humidity conditions facilitate the airborne survival and transmission of the influenza virus in temperate regions, resulting in annual winter epidemics. However, this relationship is unlikely to account for the epidemiology of influenza in tropical and subtropical regions where epidemics often occur during the rainy season or transmit year-round without a well-defined season. We assessed the role of specific humidity and other local climatic variables on influenza virus seasonality by modeling epidemiological and climatic information from 78 study sites sampled globally. We substantiated that there are two types of environmental conditions associated with seasonal influenza epidemics: "cold-dry" and "humid-rainy". For sites where monthly average specific humidity or temperature decreases below thresholds of approximately 11-12 g/kg and 18-21°C during the year, influenza activity peaks during the cold-dry season (i.e., winter) when specific humidity and temperature are at minimal levels. For sites where specific humidity and temperature do not decrease below these thresholds, seasonal influenza activity is more likely to peak in months when average precipitation totals are maximal and greater than 150 mm per month. These findings provide a simple climate-based model rooted in empirical data that accounts for the diversity of seasonal influenza patterns observed across temperate, subtropical and tropical climates.

A country bug in the city: urban infestation by the Chagas disease vector Triatoma infestans in Arequipa, Peru.

BACKGROUND: Interruption of vector-borne transmission of Trypanosoma cruzi remains an unrealized objective in many Latin American countries. The task of vector control is complicated by the emergence of vector insects in urban areas. METHODS: Utilizing data from a large-scale vector control program in Arequipa, Peru, we explored the spatial patterns of infestation by Triatoma infestans in an urban and peri-urban landscape. Multilevel logistic regression was utilized to assess the associations between household infestation and household- and locality-level socio-environmental measures. RESULTS: Of 37,229 households inspected for infestation, 6,982 (18.8%; 95% CI: 18.4 - 19.2%) were infested by T. infestans. Eighty clusters of infestation were identified, ranging in area from 0.1 to 68.7 hectares and containing as few as one and as many as 1,139 infested households. Spatial dependence between infested households was significant at distances up to 2,000 meters. Household T. infestans infestation was associated with household- and locality-level factors, including housing density, elevation, land surface temperature, and locality type. CONCLUSIONS: High levels of T. infestans infestation, characterized by spatial heterogeneity, were found across extensive urban and peri-urban areas prior to vector control. Several environmental and social factors, which may directly or indirectly influence the biology and behavior of T. infestans, were associated with infestation. Spatial clustering of infestation in the urban context may both challenge and inform surveillance and control of vector reemergence after insecticide intervention.

Assessing climate and health curriculum in graduate public health education in the United States.

Climate change has been identified as both a challenge and an opportunity for public health. The onus to prepare the next generation of public health practitioners lies heavily on schools and programs of public health. This article (i) assesses the status of climate change and health curricula in accredited schools of public health in the United States and (ii) proposes strategies to better train professionals so they are more informed and prepared to mitigate, manage, and respond to the health impacts of climate change. Course offerings and syllabi listed in online course catalogs from 90 nationally accredited schools of public health were evaluated with the purpose of identifying the extent of climate change education in graduate programs. Only 44 public health institutions were found to offer a climate change related course at the graduate level of education. Of the 103 courses identified, approximately 50% (n = 46) are focused on this climate change and health. These courses cover a wide array of topics with an emphasis on conveying fundamental concepts. In-depth assessment revealed a need for integrating learning opportunities that build practical skills useful in a hands-on public health practice environment. This assessment indicates the limited availability of climate-health course offerings available to graduate students in accredited schools. The findings are used to propose an educational framework to integrate climate change into public health curricula. The proposed framework, while rooted in existing directives, adopts a tiered approach that can be readily applied by institutions training the next generation of public health leaders.

No Consistent Link Between Dust Storms and Valley Fever (Coccidioidomycosis).

Dust storms, such as those associated with haboobs and strong regional winds, are frequently assumed to cause increases in cases of Valley fever (coccidioidomycosis). The disease is caused by inhaling arthroconidia of Coccidioides fungi that, after being disturbed from semi-desert subsoil, have become airborne. Fungal arthroconidia can be transported in low-wind conditions as well as in individual dust events, but there is no reliable evidence that all or most dust storms consistently lead to subsequent increases in coccidioidomycosis cases. Following a review of the relevant literature, this study examines the relationship between dust storms and coccidioidomycosis cases to determine if there is a consistent and general association between them. All recorded dust storms from 2006 to 2020 in and near the Phoenix area of Maricopa County, Arizona and the Bakersfield area of Kern County, California were used in a compositing analysis (superposed epoch analysis) of subsequent coccidioidomycosis cases in each area. Analyses of monthly and weekly disease case data showed no statistical differences in the patterns of coccidioidomycosis cases following dust storms versus non-dust storm conditions, for the entire data set as well as for seasonal subsets of the data. This study thoroughly analyzes post-dust storm coccidioidomycosis cases for a large set of dust storms, and it confirms and expands upon previous literature, including a recent study that measured airborne arthroconidia and found no consistent links connecting wind and dust conditions to increases in coccidioidomycosis.

Modeled response of the West Nile virus vector Culex quinquefasciatus to changing climate using the dynamic mosquito simulation model.

Climate can strongly influence the population dynamics of disease vectors and is consequently a key component of disease ecology. Future climate change and variability may alter the location and seasonality of many disease vectors, possibly increasing the risk of disease transmission to humans. The mosquito species Culex quinquefasciatus is a concern across the southern United States because of its role as a West Nile virus vector and its affinity for urban environments. Using established relationships between atmospheric variables (temperature and precipitation) and mosquito development, we have created the Dynamic Mosquito Simulation Model (DyMSiM) to simulate Cx. quinquefasciatus population dynamics. The model is driven with climate data and validated against mosquito count data from Pasco County, Florida and Coachella Valley, California. Using 1-week and 2-week filters, mosquito trap data are reproduced well by the model (P < 0.0001). Dry environments in southern California produce different mosquito population trends than moist locations in Florida. Florida and California mosquito populations are generally temperature-limited in winter. In California, locations are water-limited through much of the year. Using future climate projection data generated by the National Center for Atmospheric Research CCSM3 general circulation model, we applied temperature and precipitation offsets to the climate data at each location to evaluate mosquito population sensitivity to possible future climate conditions. We found that temperature and precipitation shifts act interdependently to cause remarkable changes in modeled mosquito population dynamics. Impacts include a summer population decline from drying in California due to loss of immature mosquito habitats, and in Florida a decrease in late-season mosquito populations due to drier late summer conditions.

Assessment of climate-coccidioidomycosis model: model sensitivity for assessing climatologic effects on the risk of acquiring coccidioidomycosis.

Understanding the predictive relationships between climate variability and coccidioidomycosis is of great importance for the development of an effective public health decision-support system. Preliminary regression-based climate modeling studies have shown that about 80% of the variance in seasonal coccidioidomycosis incidence for southern Arizona can be explained by precipitation and dust-related climate scenarios prior to and concurrent with outbreaks. In earlier studies, precipitation during the normally arid foresummer 1.5-2 years prior to the season of exposure was found to be the dominant predictor. Here, the sensitivity of the seasonal modeling approach is examined as it relates to data quality control (QC), data trends, and exposure adjustment methodologies. Sensitivity analysis is based on both the original period of record, 1992-2003, and updated coccidioidomycosis incidence and climate data extending the period of record through 2005. Results indicate that models using case-level data exposure adjustment do not suffer significantly if individual case report data are used "as is." Results also show that the overall increasing trend in incidence is beyond explanation through climate variability alone. However, results also confirm that climate accounts for much of the coccidioidomycosis incidence variability about the trend from 1992 to 2005. These strongly significant relationships between climate conditions and coccidioidomycosis incidence obtained through regression modeling further support the dual "grow and blow" hypothesis for climate-related coccidioidomycosis incidence risk.

An Analysis of the Potential Impact of Climate Change on Dengue Transmission in the Southeastern United States.

BACKGROUND: Dengue fever, caused by a mosquito-transmitted virus, is an increasing health concern in the Americas. Meteorological variables such as temperature and precipitation can affect disease distribution and abundance through biophysical impacts on the vector and on the virus. Such tightly coupled links may facilitate further spread of dengue fever under a changing climate. In the southeastern United States, the dengue vector is widely established and exists on the current fringe of dengue transmission. OBJECTIVES: We assessed projected climate change-driven shifts in dengue transmission risk in this region. METHODS: We used a dynamic mosquito population and virus transmission model driven by meteorological data to simulate Aedes aegypti populations and dengue cases in 23 locations in the southeastern United States under current climate conditions and future climate projections. We compared estimates for each location with simulations based on observed data from San Juan, Puerto Rico, where dengue is endemic. RESULTS: Our simulations based on current climate data suggest that dengue transmission at levels similar to those in San Juan is possible at several U.S. locations during the summer months, particularly in southern Florida and Texas. Simulations that include climate change projections suggest that conditions may become suitable for virus transmission in a larger number of locations and for a longer period of time during each year. However, in contrast with San Juan, U.S. locations would not sustain year-round dengue transmission according to our model. CONCLUSIONS: Our findings suggest that Dengue virus (DENV) transmission is limited by low winter temperatures in the mainland United States, which are likely to prevent its permanent establishment. Although future climate conditions may increase the length of the mosquito season in many locations, projected increases in dengue transmission are limited to the southernmost locations.

Effect of Temperature Thresholds on Modeled Aedes aegypti (Diptera: Culicidae) Population Dynamics.

Dynamic simulation models provide vector abundance estimates using only meteorological data. However, model outcomes may heavily depend on the assumptions used to parameterize them. We conducted a sensitivity analysis for a model of Aedes aegypti (L.) abundance using weather data from two locations where this vector is established, La Margarita, Puerto Rico and Tucson, Arizona. We tested the effect of simplifying temperature-dependent development and mortality rates and of changing development and mortality thresholds as compared with baselines estimated using biophysical models. The simplified development and mortality rates had limited effect on abundance estimates in either location. However, in Tucson, where the vector is established but has not transmitted viruses, a difference of 5 °C resulted in populations either surviving or collapsing in the hot Arizona mid-summer, depending on the temperature thresholds. We find three important implications of the observed sensitivity to temperature thresholds. First, this analysis indicates the need for better estimates of the temperature tolerance thresholds to refine entomologic risk mapping for disease vectors. Second, our results highlight the importance of extreme temperatures on vector survival at the marginal areas of this vector's distribution. Finally, the model suggests that adaptation to warmer temperatures may shift regions of pathogen transmission.

Habitat and Density of Oviposition Opportunity Influences Aedes aegypti (Diptera: Culicidae) Flight Distance.

Understanding the dispersal of Aedes (aegypti (L.) Diptera: Culicidae) after consuming a potentially infectious bloodmeal is an important part of controlling the spread of the arboviruses it transmits. Because of the impact on abundance, removal of oviposition sites is a key component of vector control. However, source reduction around a case may encourage dispersal of potentially infected vectors. We compare the effect of oviposition site availability on Ae. aegypti dispersal behavior within 30-m linear cages in three model ecosystems at the University of Arizona's Biosphere 2 research facility. We found a significant interaction effect in which, when oviposition site density was sparse, dispersal was greater in the highly vegetated humid rainforest and limited in the low vegetation, arid desert model ecosystem. When oviposition site density was dense, no significant effect on dispersal was observed. These analyses support the idea that source reduction has an important influence on the distance that gravid, potentially infected, females will travel.

Climate factors influencing coccidioidomycosis seasonality and outbreaks.

Although broad links between climatic factors and coccidioidomycosis have been established, the identification of simple and robust relationships linking climatic controls to seasonal timing and outbreaks of the disease has remained elusive. Using an adaptive data-oriented method for estimating date of exposure, in this article I analyze hypotheses linking climate and dust to fungal growth and dispersion, and evaluate their respective roles for Pima County, Arizona. Results confirm a strong bimodal disease seasonality that was suspected but not previously seen in reported data. Dispersion-related conditions are important predictors of coccidioidomycosis incidence during fall, winter, and the arid foresummer. However, precipitation during the normally arid foresummer 1.5-2 years before the season of exposure is the dominant predictor of the disease in all seasons, accounting for half of the overall variance. Cross-validated models combining antecedent and concurrent conditions explain 80% of the variance in coccidioidomycosis incidence. .

Extending the Kolmogorov-Zurbenko filter: application to ozone, particulate matter, and meteorological trends.

Tropospheric ozone (O3) and particulate matter (PM) are pollutants of great concern to air quality managers. Federal standards for these pollutants have been promulgated in recent years because of the known adverse effects of the pollutants on human health, the environment, and visibility. Local meteorological conditions exert a strong influence over day-to-day variations in pollutant concentrations; therefore, the meteorological signal must be removed in order for air quality planners and managers to examine underlying emissions-related trends and make better air quality management decisions for the future. Although the Kolmogorov-Zurbenko (KZ) filter has been widely used for this type of trend separation in O3 studies in the eastern United States, this article aims to extend the method in three key ways. First, whereas the KZ filter is known as a useful tool for O3 analysis, this study also evaluates its effectiveness when applied to PM. Second, the method was applied to Tucson, AZ, a city in the semi-arid southwestern United States (Southwest), to evaluate the appropriateness of the method in a region with weaker synoptic weather controls on air quality than the eastern United States. Third, additional forms of output were developed and tailored to be more applicable to decision-makers' needs through a partnership between academic researchers and air quality planners and managers. Results of the study indicate that the KZ filter is a useful method for examining emissions-related PM trends, resulting in small, but potentially significant, differences after adjustment. For the Tucson situation with weaker synoptic controls, the KZ method identified mixing height as a more important variable than has been found in other cities.

Projection of Climate Change Influences on U.S. West Nile Virus Vectors.

While estimates of the impact of climate change on health are necessary for health care planners and climate change policy makers, models to produce quantitative estimates remain scarce. We describe a freely available dynamic simulation model parameterized for three West Nile virus vectors, which provides an effective tool for studying vector-borne disease risk due to climate change. The Dynamic Mosquito Simulation Model is parameterized with species specific temperature-dependent development and mortality rates. Using downscaled daily weather data, we estimate mosquito population dynamics under current and projected future climate scenarios for multiple locations across the country. Trends in mosquito abundance were variable by location, however, an extension of the vector activity periods, and by extension disease risk, was almost uniformly observed. Importantly, mid-summer decreases in abundance may be off-set by shorter extrinsic incubation periods resulting in a greater proportion of infective mosquitoes. Quantitative descriptions of the effect of temperature on the virus and mosquito are critical to developing models of future disease risk.

Predictive mapping of air pollution involving sparse spatial observations.

A limited number of sample points greatly reduces the availability of appropriate spatial interpolation methods. This is a common problem when one attempts to accurately predict air pollution levels across a metropolitan area. Using ground-level ozone concentrations in the Tucson, Arizona, region as an example, this paper discusses the above problem and its solution, which involves the use of linear regression. A large range of temporal variability is used to compensate for sparse spatial observations (i.e. few ozone monitors). Gridded estimates of emissions of ozone precursor chemicals, which are developed, stored, and manipulated within a geographic information system, are the core predictor variables in multiple linear regression models. Cross-validation of the pooled models reveals an overall R2 of 0.90 and approximately 7% error. Composite ozone maps predict that the highest ozone concentrations occur in a monitor-less area on the eastern edge of Tucson. The maps also reveal the need for ozone monitors in industrialized areas and in rural, forested areas.

Real-time ozone mapping using a regression-interpolation hybrid approach, applied to Tucson, Arizona.

Real-time ozone (O3) maps, intended for public access and mass media, are generated from spatially interpolating (i.e., kriging) sparse monitoring data and are typically characterized by over-smoothed surfaces that inadequately represent local-scale spatial patterns (e.g., averaged over 1 km2). In this paper, a hybrid regression-interpolation methodology is developed to enhance the representation of local-scale spatiotemporal patterns with an application to Tucson, Arizona. The mapping of local patterns is enhanced with pre-interpolation regression modeling of local-scale deviation-from-mean variability, preserving variation in the monitor data that is ubiquitous across the modeling domain (i.e., the areal mean). The model is trained on several years of deviation-from-mean hourly O3 data, and predictor variables are developed using theoretically and empirically derived proxy regression variables. The regression model explains a significant proportion of the variation in the data (r2 = 0.54), with an average error of 7.1 ppb. When augmented with the areal mean, the r2 of the pre-interpolation model increases to 0.847. Model residuals are then spatially interpolated to the extents of the modeling domain. Final concentration estimate maps are the summation of areal mean, regression, and spatially interpolated surfaces, preserving absolute values at monitor locations.

Climate and dengue transmission: evidence and implications.

BACKGROUND: Climate influences dengue ecology by affecting vector dynamics, agent development, and mosquito/human interactions. Although these relationships are known, the impact climate change will have on transmission is unclear. Climate-driven statistical and process-based models are being used to refine our knowledge of these relationships and predict the effects of projected climate change on dengue fever occurrence, but results have been inconsistent. OBJECTIVE: We sought to identify major climatic influences on dengue virus ecology and to evaluate the ability of climate-based dengue models to describe associations between climate and dengue, simulate outbreaks, and project the impacts of climate change. METHODS: We reviewed the evidence for direct and indirect relationships between climate and dengue generated from laboratory studies, field studies, and statistical analyses of associations between vectors, dengue fever incidence, and climate conditions. We assessed the potential contribution of climate-driven, process-based dengue models and provide suggestions to improve their performance. RESULTS AND DISCUSSION: Relationships between climate variables and factors that influence dengue transmission are complex. A climate variable may increase dengue transmission potential through one aspect of the system while simultaneously decreasing transmission potential through another. This complexity may at least partly explain inconsistencies in statistical associations between dengue and climate. Process-based models can account for the complex dynamics but often omit important aspects of dengue ecology, notably virus development and host-species interactions. CONCLUSION: Synthesizing and applying current knowledge of climatic effects on all aspects of dengue virus ecology will help direct future research and enable better projections of climate change effects on dengue incidence.

Climatic controls on West Nile virus and Sindbis virus transmission and outbreaks in South Africa.

The processes influencing the magnitude of West Nile virus (WNV) transmission from 1 year to the next require thorough investigation. The intensity of WNV transmission is related to the dynamics and interactions between the pathogen, vector, vertebrate hosts, and environment. Climatic variability is one process that can influence interannual disease transmission. South Africa has a long WNV and Sindbis virus (SINV) record where consistent climate and disease relationships can be identified. We relate climate conditions to historic mosquito infection rates. Next, we detect similar associations with reported human outbreaks dating back to 1941. Both concurrent summer precipitation and the change in summer precipitation from the previous to the current summer were strongly associated with WNV and SINV transmission and recorded human outbreaks. Each 100 mm interannual summer precipitation change increased WNV infection rates by 0.39 WNV-positive Culex univittatus/1000 tested Cx. univittatus. An improved understanding of biotic and abiotic disease transmission dynamics may help anticipate and mitigate future outbreaks.

Coccidioidomycosis incidence in Arizona predicted by seasonal precipitation.

The environmental mechanisms that determine the inter-annual and seasonal variability in incidence of coccidioidomycosis are unclear. In this study, we use Arizona coccidioidomycosis case data for 1995-2006 to generate a timeseries of monthly estimates of exposure rates in Maricopa County, AZ and Pima County, AZ. We reveal a seasonal autocorrelation structure for exposure rates in both Maricopa County and Pima County which indicates that exposure rates are strongly related from the fall to the spring. An abrupt end to this autocorrelation relationship occurs near the the onset of the summer precipitation season and increasing exposure rates related to the subsequent season. The identification of the autocorrelation structure enabled us to construct a "primary" exposure season that spans August-March and a "secondary" season that spans April-June which are then used in subsequent analyses. We show that October-December precipitation is positively associated with rates of exposure for the primary exposure season in both Maricopa County (R = 0.72, p = 0.012) and Pima County (R = 0.69, p = 0.019). In addition, exposure rates during the primary exposure seasons are negatively associated with concurrent precipitation in Maricopa (R = -0.79, p = 0.004) and Pima (R = -0.64, p = 0.019), possibly due to reduced spore dispersion. These associations enabled the generation of models to estimate exposure rates for the primary exposure season. The models explain 69% (p = 0.009) and 54% (p = 0.045) of the variance in the study period for Maricopa and Pima counties, respectively. We did not find any significant predictors for exposure rates during the secondary season. This study builds on previous studies examining the causes of temporal fluctuations in coccidioidomycosis, and corroborates the "grow and blow" hypothesis.

A history of chagas disease transmission, control, and re-emergence in peri-rural La Joya, Peru.

BACKGROUND: The history of Chagas disease control in Peru and many other nations is marked by scattered and poorly documented vector control campaigns. The complexities of human migration and sporadic control campaigns complicate evaluation of the burden of Chagas disease and dynamics of Trypanosoma cruzi transmission. METHODOLOGY/PRINCIPAL FINDINGS: We conducted a cross-sectional serological and entomological study to evaluate temporal and spatial patterns of T. cruzi transmission in a peri-rural region of La Joya, Peru. We use a multivariate catalytic model and Bayesian methods to estimate incidence of infection over time and thereby elucidate the complex history of transmission in the area. Of 1,333 study participants, 101 (7.6%; 95% CI: 6.2-9.0%) were confirmed T. cruzi seropositive. Spatial clustering of parasitic infection was found in vector insects, but not in human cases. Expanded catalytic models suggest that transmission was interrupted in the study area in 1996 (95% credible interval: 1991-2000), with a resultant decline in the average annual incidence of infection from 0.9% (95% credible interval: 0.6-1.3%) to 0.1% (95% credible interval: 0.005-0.3%). Through a search of archival newspaper reports, we uncovered documentation of a 1995 vector control campaign, and thereby independently validated the model estimates. CONCLUSIONS/SIGNIFICANCE: High levels of T. cruzi transmission had been ongoing in peri-rural La Joya prior to interruption of parasite transmission through a little-documented vector control campaign in 1995. Despite the efficacy of the 1995 control campaign, T. cruzi was rapidly reemerging in vector populations in La Joya, emphasizing the need for continuing surveillance and control at the rural-urban interface.

An epidemic of coccidioidomycosis in Arizona associated with climatic changes, 1998-2001.

BACKGROUND: Reports of coccidioidomycosis cases in Arizona have increased substantially. We investigated factors associated with the increase. METHODS: We analyzed the National Electronic Telecommunications System for Surveillance (NETSS) data from 1998 to 2001 and used Geographic Information Systems (GIS) to map high-incidence areas in Maricopa County. Poisson regression analysis was performed to assess the effect of climatic and environmental factors on the number of monthly cases; a model was developed and tested to predict outbreaks. RESULTS: The overall incidence in 2001 was 43 cases/100,000 population, a significant (P<.01, test for trend) increase from 1998 (33 cases/100,000 population); the highest age-specific rate was in persons > or =65 years old (79 cases/100,000 population in 2001). Analysis of NETSS data by season indicated high-incidence periods during the winter (November-February). GIS analysis showed that the highest-incidence areas were in the periphery of Phoenix. Multivariable Poisson regression modeling revealed that a combination of certain climatic and environmental factors were highly correlated with seasonal outbreaks (R2=0.75). CONCLUSIONS: Coccidioidomycosis in Arizona has increased. Its incidence is driven by seasonal outbreaks associated with environmental and climatic changes. Our study may allow public-health officials to predict seasonal outbreaks in Arizona and to alert the public and physicians early, so that appropriate preventive measures can be implemented.